A Pattern Language for

New Media

Gideon Rosenblatt

Six Functional

Blocks

Organized in many different

configurations.

Identity

ViewingTopic GraphPublishing

RevenueSocial Graph

Publishing Content

Key Variables:• Sophistication• Openness

Tools and services for creating and publishing text, audio, and video content.

Publishing Sophistication

Twitter

Facebook

Google+

LinkedIn

WordPress

Simpler

More Complex

(editing, formatting, etc.)

Social Media

Publishing Openness(who has access to the ‘publish’ button?)

Select FewEveryone

Blogs Magazines

Viewing Content

Key Variables:• Filtering

Tools and services for filtering, viewing, sharing and providing feedback on content. Includes social media feeds, search results pages, and aggregating websites.

Algorithms Using Social and Topic

Graphs

Filtering Content(who or what filters the content we view?)

Professional FilteringMachine Filtering

Magazine Editors

Community & List Moderators

Machine Filtering(how do we filter the content we view?)

Social InterestTopical Interest

Topic Graph Social Graph

Attaching Topics to Content in Publishing

Publishing

We attach topics to content with categories, tags, keywords, groups, communities and hashtags.

Topic Graph

Attaching Topics to Content After Publishing

Google crawls our content to assess its relevance to various topics. It also matches what it finds against its Knowledge Graph for more accurate relevance assessment.

Topic Graph

Knowledge Graph

Search Bot

Identity: More than Just a Pretty Face

Identity

Like many things on the web, our identity is not unlike an iceberg; only a small portion of it is visible to us as our profile. The rest resides in massive data sets stored on the proprietary servers of new media companies.

Attaching Topics to Identity: the Interest Graph

Identity Topic Graph

Some of that data relates to our interests. We attach topics to our identity every time we like, plus, tweet, share or search for something online. The result is our own, individual interest graph.

Interest Graph

Viewing Filtered by Interests

ViewingInterest Graph

Our Interest Graph personalizes our search results and social media streams to help us stay on top of our interests.

Relationships and Identity: the Social Graph

Identity Social Graph

Some of our identity data relates to our relationships. We build our social graph by harvesting our past through school and work connections, crawling lists of friends of friends, and making new connections through communities and other online interactions.

Viewing Filtered by Relationships

ViewingSocial Graph

Our Social Graph personalizes our social media streams to help us tend to our relationships.

Connecting the Topic and Social Graphs

+ =

Social Graph Interest GraphShared Interest Graph

Our Shared Interest Graph helps us find and build relationships with people who share our interests.

Revenue from New Media

> Social GraphInterest Graph

Ads and subscriptions drive most media revenues. Ads work best when tied to our interests, but are largely noise when we’re socializing.

Subscription revenues are unlikely from the social graph, unless paired with interests, as in certain shared interest graphs.

with Revenues,

End User Engagement is the Super Power

The following is a simple attempt to apply this pattern language to a handful of representative new media organizations. Of course, others will have a very different interpretation of these organizations.

Publishing SocialGraph

InterestGraph

Revenues SharedInterestGraph

TopicGraph

Viewing

Google ID

main stream, circle

streams,communities,

Google search

good search, communities,

circles, hashtags

posts and comment

s

no revenues,may help

search revenues

…shared interest graph

Google PlusShared interest network with civil discussion and no revenues. 300 million users.

strangers become friends through a

…

Google ID

search results pages

search bots, algorithms,

Knowledge Graph

SEO optimization, Ad

Sense

search advertisingSearch,

plus Your World

Google SearchPowerful media aggregator, topic graph and ad network. 1.17 billion users

relevance algorithms,

personalized search

Facebook IDrich demographics

stream, groups

Open Graph objects

posts & comments, Open

Graph for 3rd party sites

on-site ads, mobile app Audience Network

friends

FacebookPowerful social network with rich user information. 1.3 billion users.

interests

groups

LinkedIn IDrich professional

history

stream, groups

companies, schools occupations,

industries

updates, rich posts &

comments

ads to professional audience, premium servicesprofession

al connection

s

LinkedInPowerful professional network and interest graph. 300 million users.

interests

school & organizational

alumni

weak identity

strong crowd filtering, minimal

algorithmic filtering

categories, ok search

Mostly linking, lots

of comments

minimal ads, freemium

friends, but not

prominent

RedditScrappy, content-sharing network, broken out by interests. 114 million users.

3rd-party logins

mix of editorial and popularity

filtering

60 vertical sites

unpaid bloggers

ads $100 million

relies on 3rd-party social

graphs

Huffington PostNews and opinion, powered by staff-coordinated, unpaid writers. 84 million users.

WordPress login

WordPress Reader

categories, tags,

keywords

rich editing, commentin

g

freemium, ad-on services

relies on 3rd-party social

graphs

WordPressBlogging / Content Management tool. 77 million websites (22% of worldwide total).

End User Engagement as Super Power

The real difference with new media is that it is a two-way channel, capable of engaging people in a new collaborative partnership.

New media success depends on building business models to engage end users in publishing and viewing media, and building social, topical, interest and shared interest graphs.

This is the goose that lays the golden eggs of new media.

For more:

A Pattern Language

for New Media

http://www.the-vital-edge.com/new-media/